Cracking the fraudulent-data code

A study published by the Proceedings of the National Academy of Science found that 43.4% of retracted articles were pulled over false and fabricated data or suspicion of fraud, while errors accounted for only 21.3% of the 2,047 retracted articles listed in PubMed's index as of March 3. The study noted that PubMed references 25 million articles, going back to the 1940s.

The authors wrote that even when journals talk about false data, it can be in code. Case in point: when the journal Biochemical and Biophysical Research Communications discovered a 1991 article it published included false and fabricated information, its retraction read: “results were derived from experiments that were found to have flaws in methodological execution and data analysis.” The PNAS study's authors discovered the real reason—fraud—because Harvard reported it to the Office of Research Integrity (ORI) and the PNAS authors read Harvard's report. The Office of Research Integrity is part of the Department of Health and Human Services and investigates allegations of research misconduct.

This year's ORI tally includes a case in which an accused researcher “neither admits nor denies committing research misconduct but accepts ORI has found evidence of research misconduct.” ORI closed 13 investigations last year, eight in 2010 and seven in 2009. Punishment can include avoiding government-contracted research jobs and having research vetted by a third party for a specified period of time.

The September 2011 ORI newsletter, which also serves as a printed record of case findings, acknowledged that journals may hold off publishing a retraction in the interests of being fair, yet also noted that even if an institution weighs in and an investigation closes, “a retraction may never be published, because of other factors.”

Like the PNAS researchers, the Office of Research Integrity also noted that “the text of the retractions, corrections or errata associated with falsified papers rarely explicates the details on which components of a study are false, and/or why.”

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